Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle

Unobtrusive in-vehicle health monitoring has the potential to use the driving time to perform regular medical check-ups. This work intends to provide a guide to currently proposed sensor systems for in-vehicle monitoring and to answer, in particular, the questions: (1) Which sensors are suitable for in-vehicle data collection? (2) Where should the sensors be placed? (3) Which biosignals or vital signs can be monitored in the vehicle? (4) Which purposes can be supported with the health data? We reviewed retrospective literature systematically and summarized the up-to-date research on leveraging sensor technology for unobtrusive in-vehicle health monitoring. PubMed, IEEE Xplore, and Scopus delivered 959 articles. We firstly screened titles and abstracts for relevance. Thereafter, we assessed the entire articles. Finally, 46 papers were included and analyzed. A guide is provided to the currently proposed sensor systems. Through this guide, potential sensor information can be derived from the biomedical data needed for respective purposes. The suggested locations for the corresponding sensors are also linked. Fifteen types of sensors were found. Driver-centered locations, such as steering wheel, car seat, and windscreen, are frequently used for mounting unobtrusive sensors, through which some typical biosignals like heart rate and respiration rate are measured. To date, most research focuses on sensor technology development, and most application-driven research aims at driving safety. Health-oriented research on the medical use of sensor-derived physiological parameters is still of interest

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Huomaamattomat mittausmenetelmät unen laadun tarkkailussa

Sleep is an important part of health and well-being. While sleep quantity is directly measurable, sleep quality has traditionally been assessed with subjective methods such as questionnaires. The study of sleep disorders has for a long time been confined to clinical environments, and patients have had to endure cumbersome procedures involving multiple electrodes placed on the body. Recent developments in sensor technology as well as data analysis methods have enabled continuous, unobtrusive sleep data recording in the home environment. This has opened new possibilities for studying various sleep parameters and their effect on the quality of sleep. This thesis consists of two parts. The first part is a literature review examining the field of sleep quality research with focus on the application of intelligent methods and signal processing. The second part is a descriptive data analysis look at sleep data obtained with non-invasive sensors.Uni on terveyden ja hyvinvoinnin keskeinen tekijä. Unen määrä on helposti mitattavissa, mutta unen laatua on perinteisesti seurattu kyselylomakkeiden kaltaisin subjektiivisin menetelmin. Unihäiriöiden tutkiminen on pitkään rajoittunut kliinisiin ympäristöihin, ja potilaiden on täytynyt sietää hankalia tutkimusmenetelmiä useine kehoon kiinnitettävine elektrodeineen. Anturiteknologian ja data-analyysimenetelmien kehittyminen on mahdollistanut unidatan jatkuvan ja huomaamattoman tallentamisen kotiympäristössä. Tämä on avannut uusia mahdollisuuksia sekä unen ominaisuuksien että niiden unen laatuun vaikuttavien tekijöiden tutkimiselle. Tämä tutkimus jakautuu kahteen osaan. Ensimmäinen osa on kirjallisuuskatsaus unen laadun tutkimukseen, painopisteenä älykkäiden menetelmien ja signaalinkäsittelyn soveltaminen. Toisessa osassa esitellään huomaamattomilla sensoreilla kerättävän unidatan tutkimista ja sen deskriptiivistä data-analyysiä, esimerkkinä ballistokardiografia

Physiological and behavior monitoring systems for smart healthcare environments: a review

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressedinfo:eu-repo/semantics/publishedVersio

Physiological Information Analysis Using Unobtrusive Sensors: BCG from Load-Cell Based Infants' Bed and ECG from Patch Electrode

학위논문 (박사)-- 서울대학교 대학원 : 바이오엔지니어링전공, 2016. 8. 박광석.The aging population, chronic diseases, and infectious diseases are major challenges for our current healthcare system. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, acquiring physiological information of different types has emerged as a promising interdisciplinary research area. Unobtrusive sensing techniques are instrumental in constructing a routine health management system, because they can be incorporated in daily life without confining individuals or causing any discomfort. This dissertation is dedicated to summarizing our research on monitoring of cardiorespiratory activities by means of unobtrusive sensing methods. Ballistocardiography and electrocardiography, which record the activity of the cardiorespiratory system with respect to mechanical or electrical characteristics, are both being actively investigated as important physiological signal measurement that provide the information required to monitor human health states. This research was carried out to evaluate the feasibility of new application methods of unobtrusive sensing that not been investigated significantly in previous investigations. We also tried to incorporate improvement essential for bringing these technologies to practical use. Our first device is a non-confining system for monitoring the physiological information of infants using ballistocardiography technology. Techniques to observe continuous biological signals without confinement may be even more important for infants since they could be used effectively to detect respiratory distress and cardiac abnormalities. We also expect to find extensive applications in the field of sleep research for analyzing sleep efficiency and sleep patterns of infants. Specifically, the sleep of infants is closely related to their health, growth, and development. Children who experience abnormal sleep and activity rhythms during their early infantile period are more prone to developing sleep-related disorders in late childhood, which are also more difficult to overcome. Therefore, studying their sleep characteristics is extremely important. Although ballistocardiography technology seems to represent a possible solution to overcome the limitations of conventional physiological signal monitoring, most studies investigating the application of these methods have focused on adults, and few have been focused on infants. To verify the usefulness of ballistocardiogram (BCG)-based physiological measurement in infants, we describe a load-cell based signal monitoring bed and assess an algorithm to estimate heartbeat and respiratory information. Four infants participated in 13 experiments. As a reference signal, electrocardiogram (ECG) and respiration signals were simultaneously measured using a commercial device. The proposed automatic algorithm then selected the optimal sensor from which to estimate the heartbeat and respiratory information. The results from the load-cell sensor signals were compared with those of the reference signals, and the heartbeat and respiratory information were found to have average performance errors of 2.55% and 2.66%, respectively. We believe that our experimental results verify the feasibility of BCG-based measurements in infants. Next, we developed a small, light, ECG monitoring device with enhanced portability and wearability, with software that contains a peak detection algorithm for analyzing heart rate variability (HRV). A mobile ECG monitoring system, which can assess an individuals condition efficiently during daily life activities, could be beneficial for management of their health care. A portable ECG monitoring patch with a minimized electrode array pad, easily attached to a persons chest, was developed. To validate the devices performance and efficacy, signal quality analysis in terms of robustness under motion, and HRV results obtained under stressful conditions were assessed by comparing the developed device with a commercially available ECG device. The R-peak detection results obtained with the device exhibited a sensitivity of 99.29%, a positive predictive value of 100.00%, and an error of 0.71%. The device also exhibited less motional noise than conventional ECG recording, being stable up to a walking speed of 5 km/h. When applied to mental stress analysis, the device evaluated the variation in HRV parameters in the same way as a reference ECG signal, with very little difference. Thus, our portable ECG device with its integrated minimized electrode patch carries promise as a form of ECG measurement technology that can be used for daily health monitoring. There is currently an increased demand for continuous health monitoring systems with unobtrusive sensors. All of the experimental results in this dissertation verify the feasibility of our unobtrusive cardiorespiratory activity monitoring system. We believe that the proposed device and algorithm presented here are essential prerequisites toward substantiating the utility of unobtrusive physiological measurements. We also expect this system can help users better understand their state of health and provide physicians with more reliable data for objective diagnosis.Chapter 1. Introduction 1 1.1. Cardiorespiratory signal and its related physiological information 2 1.1.1. Electrocardiogram 2 1.1.2. Ballistocardiogram 3 1.1.3. Respiration 4 1.1.4. Heart rate and breathing rate 5 1.1.5. Variability analysis of heart and respiratory rate 5 1.2. Unobtrusive sensing methods for continuous physiological monitoring 6 1.3. Outline of the dissertation 9 Chapter 2. Development of sensor device for unobtrusive physiological signal measurement 13 2.1. Unobtrusive BCG measurement device for infants health monitoring 13 2.1.1. Specifications of the device 17 2.1.2. Signal processing in hardware 18 2.1.3. Performance of the device 21 2.2. Unobtrusive ECG measurement device for health monitoring in daily life 25 2.2.1. Specifications of the device 26 2.2.2. Signal processing in hardware 28 2.2.3. Performance of the device 30 Chapter 3. Development of algorithm for physiological information analysis from unobtrusively measured signal 35 3.1. Algorithm for automatically analyzing unobtrusively measured BCG signal 35 3.1.1. Process flow of the algorithm 36 3.1.2. Performance evaluation 47 3.2. Algorithm for automatically analyzing unobtrusively measured ECG signal 57 3.2.1. Process flow of the algorithm 57 3.2.2. Performance evaluation 60 3.3. HRV analysis for processing unobtrusively measured signals 63 3.3.1. Optimum HRV algorithm selection in data missing simulation 64 3.3.2. Stress assessment using HRV parameters 67 Chapter 4. Discussion 71 Chapter 5. Conclusion 79 Reference 81 Abstract in Korean 89 Appendix 93Docto

Driving Monitoring System Application With Stretchable Conductive Inks: A Review

Nowadays the automotive industry is moving towards developing system connected vehicle parameters which can monitor the driver’s behaviour before driving. Most drivers lose focus and are emotionally distracted while driving owing to fatigue, drowsiness and alcohol consumption, that can result in a traffic accidents. The device or equipment used to detect the driver’s health before driving has always posed a problem in terms of the efficiency of the system especially concerning the cable connecting the equipment. Stretchable conductive ink (SCI) via electronic devices have been widely applied in various industries such as fabric, health, automotive, communications, etc. The flexibility allows a circuit to be placed on an uneven or constantly changing surface. However, till to-date, the effective use of the stretchable conductive ink has yet to be proven in the automotive industry. The current driver monitoring system cannot integrate with many of the driver's health level tracking features at one time. A combination of the driver’s monitoring system methods with stretchable conductive ink (SCI) sensors layout design can be used to prevent road accidents as a result of a driver’s behavior and will make the driving monitoring system more effective with soft substrates technology that has the advantage of geometric deformation based on appropriate shapes

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 138 reports, articles, and other documents introduced into the NASA scientific and technical information system in Jun. 1980

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic